package ds_industry_2025.industry.gy_04.T3

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

/*
      4、编写Scala代码，使用Spark根据dwd_ds_hudi层的fact_produce_record表，基于全量历史数据计算各设备生产一个产品的平均耗
      时，produce_code_end_time值为1900-01-01 00:00:00的数据为脏数据，需要剔除（注：fact_produce_record表中，一条数据代
      表加工一个产品，produce_code_start_time字段为开始加工时间，produce_code_end_time字段为完成加工时间），将设备每个产
      品的耗时与该设备平均耗时作比较，保留耗时高于平均值的产品数据，将得到的数据写入ClickHouse数据库shtd_industry
      的machine_produce_per_avgtime表中（表结构如下），然后在Linux的ClickHouse命令行中根据设备id降序排序查询前3条数据，将
      SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌面【Release\任
      务B提交结果.docx】中对应的任务序号下；
 */
object t7 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("t7")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .config("spark.sql.legacy.parquet.datetimeRebaseModeInRead","LEGACY")
      .enableHiveSupport()
      .getOrCreate()


    val hdfs="hdfs://192.168.40.110:9000/user/hive/warehouse/hudi_gy_dwd.db/fact_produce_record"

    spark.read.format("hudi").load(hdfs)
      .where(col("producecodeendtime") !== "1900-01-01 00:00:00")
      .createOrReplaceTempView("data")

    val result = spark.sql(
      """
        |select
        |produce_record_id,
        |produce_machine_id,
        |time as producetime,
        |avg_time as produce_per_avgtime
        |from(
        |select distinct
        |produce_record_id,
        |produce_machine_id,
        |time,
        |avg(time) over(partition by produce_machine_id) as avg_time
        |from(
        |select
        |producerecordid as produce_record_id,
        |producemachineid as produce_machine_id,
        |(unix_timestamp(producecodeendtime) - unix_timestamp(producecodestarttime)) as  time
        |from data
        |) as r1
        |) as r2
        |where time > avg_time
        |""".stripMargin)


    result.show
//    result.write.format("jdbc")
//      .option("url","jdbc:clickhouse://192.168.40.110:8123/shtd_industry")
//      .option("user","default")
//      .option("password","")
//      .option("driver","com.clickhouse.jdbc.ClickHouseDriver")
//      .option("dbtable","machine_produce_per_avgtime")
//      .save()

    spark.close()
  }

}
